Text Classification
Transformers
ONNX
Safetensors
multilingual
modernbert
mmbert-32k
yarn-rope
merged-model
text-embeddings-inference
Instructions to use llm-semantic-router/mmbert32k-factcheck-classifier-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llm-semantic-router/mmbert32k-factcheck-classifier-merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="llm-semantic-router/mmbert32k-factcheck-classifier-merged")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("llm-semantic-router/mmbert32k-factcheck-classifier-merged") model = AutoModelForSequenceClassification.from_pretrained("llm-semantic-router/mmbert32k-factcheck-classifier-merged", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload label_mapping.json with huggingface_hub
Browse files- label_mapping.json +1 -10
label_mapping.json
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{
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"label_to_idx": {
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"NO_FACT_CHECK_NEEDED": 0,
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"idx_to_label": {
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{"label_to_idx": {"NO_FACT_CHECK_NEEDED": 0, "FACT_CHECK_NEEDED": 1}, "idx_to_label": {"0": "NO_FACT_CHECK_NEEDED", "1": "FACT_CHECK_NEEDED"}}
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